Efficient Information Diffusion in Time-Varying Graphs through Deep Reinforcement Learning

• Published in: arXiv.org
• Main Authors: Mendonça, Matheus R F, Barreto, André M S, Ziviani, Artur
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 27.11.2020
• Subjects: Deep learning; Graphs; Information dissemination; Maximization; Optimization
• ISSN: 2331-8422

Network seeding for efficient information diffusion over time-varying graphs~(TVGs) is a challenging task with many real-world applications. There are several ways to model this spatio-temporal influence maximization problem, but the ultimate goal is to determine the best moment for a node to start the diffusion process. In this context, we propose Spatio-Temporal Influence Maximization~(STIM), a model trained with Reinforcement Learning and Graph Embedding over a set of artificial TVGs that is capable of learning the temporal behavior and connectivity pattern of each node, allowing it to predict the best moment to start a diffusion through the TVG. We also develop a special set of artificial TVGs used for training that simulate a stochastic diffusion process in TVGs, showing that the STIM network can learn an efficient policy even over a non-deterministic environment. STIM is also evaluated with a real-world TVG, where it also manages to efficiently propagate information through the nodes. Finally, we also show that the STIM model has a time complexity of \(O(|E|)\). STIM, therefore, presents a novel approach for efficient information diffusion in TVGs, being highly versatile, where one can change the goal of the model by simply changing the adopted reward function.